Analysis From The Start To The End

[kingers]

Analysis From The Start To The End
Published 3/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English

| Size: 5.16 GB[/center]
| Duration: 7h 8m
part of the Big Bang of Data Science

What you'll learn

Research map & the relevance to analysis

Threats to conclusion validity

Fundamentals of statistical powers

Improvement of conclusion validity

Map of analysis Framework

Data preparation: logging, accuracy control, structure, and transformation

LAB-01: Data Preparation; all the steps you need to prepare your data from research perspective

Fundamentals of EDA

Mass, and Density distributions

Central tendency: mean, median, mode, and proportion

Dispersion: rang, interquartile, variance, and standard deviation

Univariate, Bivariate, and Multivariate analysis- process

LAB-02: analysis using univariate, bivariate and multivariate techniques

Inferential Analysis: estimating parameters, and hypothesis testing

Statistical tests: t-test, Chi square, Pearson's, F-test, ANOVA, MANOVA, and many more

Intro to open-source statistical software Jamovi

LAB-03: implementation of all the outlines using Jamovi on synthetical datasets

LAB-04: implementation of all the outlines using Jamovi on real dataset

Requirements

No specific experience needed. ONLY, your motivation to outcome a product that is good, successful, and intelligent

STRONGLY RECOMMENDED: the completion of the first book- Research from the Start to the End.

Description

The ContentThis is the second element of the Big Bang of Data Science, that is [Analysis from the Start to The End].I don't want to stick to that abstract and direct definition from the academic book, on the meaning of analysis, but from the industrial one. So, I believe ANALYSIS is the co-concertmaster that sits in the second chair of the highest leadership position among all the other parts that are responsible for the outcome of a product. Analysis is an art that has the characteristics of being a two-edged sword. In other words, if your understanding of analysis is based on subjective, rigid ground then your answers; solutions; products are for sure questioned. However, if your analysis is based on objective, scientific grounds then your answers; solutions; products are for sure worthy of consuming. If you search any search engine the word of analysis, you should not be surprised with the astronomical number of results on your search. The problem with many of the materials which discuss the subject of analysis is that two perspectives are there:the first, the perspective of analysis as a bunch of graphs and tables,and the second, the perspective of analysis is a bunch of tests and tools that applies them.Well, one can argue there is nothing wrong with that, but the problem arises when one fails to understand the raw materials that are needed to present those tables and figures, in addition, the fundamentals of those tools and tests that produce them. To this end, this book aims to address this mis conceptual understanding about analysis; basically, the book materials are constructed in such way that one can:firstly, understand the important of data that come from solid research,secondly, to understand the fundamentals of analysis from philosophical and scientifical perspective,thirdly, complete grasp on the meaning of hypothesis, as forming, articulating, etc.,and finally, the comprehensive knowledge on the tests and tools are there to help you implement your analysis.To this end, the second book is carefully crafted to meet all the requirements to build your product on the right foundation of analysis. Here is a quick view of the content of the book.Introduction[✓] Research map[✓] THREATS TO CONCLUSION VALIDITY[✓] STATISTICAL POWER[✓] IMPOROVE CONCLUSION VALIDITY[✓] ANALYSISData Preparation[✓] LOGGING THE DATA[✓] DATA ACCURACY CONTROL[✓] DATABASE STRUCTURE[✓] ENTERING DATA TO THE COMPUTER[✓] DATA TRANSFORMATION[✓] LAB-01- Three parts- on data preparationDescriptive Statistics[✓] Introduction to EDA[✓] Distribution[✓] Central Tendency[✓] Dispersion[✓] Bivariate descriptive[✓] Multivariate descriptive[✓] LAB-02- analysis on univariate, bivariate, and multivariate Inferential Statistics[✓] Introduction[✓] Estimating Parameters[✓] Hypothesis TestingStatistical Software[✓] Introduction[✓] Statistical Software[✓] Intro- Implementation by JAMOVI[✓] LAB-03- analysis on two datasets using JAMOVILAB-Section -04- Analysis on real dataset using Jamovi[✓] Review[✓] EDA analysis[✓] Inferential Analysis[✓] LAB-04- implementation on the dataset from the first bookWho is this book for?This book is for anyone, regardless of the educational background, with the interest in building, creating and producing a professional product that has a vision of the future. You don't have to have specific skill in any way, but extreme enthusiasm to learn how to make the right decision. So, it is meant for an audience of: (1) students, under or postgraduate. (2) scholars, (3) researchers, (4) scientists, (5) executives, (6) managers, (7) professionals, (8) or laypersons.TipThe trainer strongly advice on learning the materials from the first book Research from the Start to the End; that can absolutely help you to perform way better in this book.Competitive advantages!as outlined above in the introduction, this book is the second book from The Big Bang of Data Science that means it's an element among other elements of a project. This implies that the outlines and the contents are not ONLY discussed from an analysis perspective, but also from a wider perspective of the entire project. This offers you an opportunity to excel in the subject of analysis from a wider range of disciplines.As I have outlined above in the introduction, so many materials discuss the subject of analysis, however, many of which fail to focus on the subject of orientation. If your analysis is subjective oriented, i.e., your analysis is controlled by external factors such as your background, education, environment, culture and many more, then your final solution is questionable. However, if your analysis is objective oriented, i.e., your analysis is based on methodical, and scientific facts, then your final product is worthy of consuming. This material is constructed based on the latter, that is objective oriented approach.The slogan of the Big Bang of Data Science is From academia to industry, this material is obligated to that. You will have two types of labs: the first is using synthetical type of data to implement the abstracts and theories you learn, and the second uses a real dataset that we have built from the first book Research from the Start to the End. As a result, you will master the idea from abstract to applied.Lastly, all the types of tests we are going to learn about will be executed using an open-source statistical tool, that is Jamovi. This tool offers several statistical tests that one needs to do research analysis. Notably, unlike other material that presents analysis within the framework of jamovi, this material coaches you how to understand the selection of the right test, first, then you can use this tool or any other tool of your choice to execute the test. So, this perceptive gives you confidence in relying on many other tools of your choice if you understand each test independently.

Overview

Section 1: About the trainer

Lecture 1 Dahman's Phi Services- initiative

Lecture 2 The Story of- The Big Bang of Data Science Project

Lecture 3 Introduction to- The Big Bang of Data Science- First Edition

Lecture 4 Introduction to- The Big Bang of Data Science- Second Edition

Section 2: Introduction to this course

Lecture 5 Why this course, and its competitive advantages

Lecture 6 The main contents- in form of chapters

Lecture 7 Screencast covers all the recorded lectures

Lecture 8 The PDF lecture slides

Section 3: Chapter One- Introduction

Lecture 9 Research Map & relevance to analysis

Lecture 10 Threats to conclusion validity

Lecture 11 Statistical power

Lecture 12 Improve conclusion validity

Lecture 13 Analysis framework

Section 4: Chapter Two- Data Preparation

Lecture 14 Logging the data

Lecture 15 Data accuracy control

Lecture 16 Database structure

Lecture 17 Entering data to computer

Lecture 18 Data preparation

Lecture 19 LAB-01- Data preparation- PART ONE

Lecture 20 LAB-01- Data preparation- PART TWO

Lecture 21 LAB-01- Data preparation- PART THREE

Section 5: Chapter three- Descriptive Analysis

Lecture 22 Introduction to EDA

Lecture 23 Distribution- Introduction

Lecture 24 Distribution- Mass type

Lecture 25 Distribution- Density type

Lecture 26 Central Tendency- Introduction

Lecture 27 Central Tendency- Mean

Lecture 28 Central Tendency- Median

Lecture 29 Central Tendency- Mode

Lecture 30 Central Tendency- Proportion

Lecture 31 Central Tendency- Recap

Lecture 32 Dispersion- introduction

Lecture 33 Dispersion- Range

Lecture 34 Dispersion- interquartile

Lecture 35 Dispersion- Variance & Standard Deviation

Lecture 36 Bivariate Analysis- PART ONE

Lecture 37 Bivariate Analysis- PART TWO

Lecture 38 Bivariate Analysis- PART THREE

Lecture 39 Bivariate Analysis- PART FOUR

Lecture 40 Bivariate Analysis- PART FIVE

Lecture 41 Bivariate Analysis- PART SIX

Lecture 42 Bivariate Analysis- PART SEVEN

Lecture 43 Bivariate Analysis- PART EIGHT

Lecture 44 Multivariate Analysis- Review

Lecture 45 LAB-02- Analysis; univariate, bivariate and multivariate

Section 6: Chapter Four- Inferential Analysis

Lecture 46 Introduction to Inferential analysis

Lecture 47 Estimating parameters- Point estimate

Lecture 48 Estimating Parameters- Interval estimate

Lecture 49 hypothesis testing- Introduction

Lecture 50 hypothesis testing- factors for selection tests- PART ONE

Lecture 51 hypothesis testing- factors for selection tests- PART TWO

Section 7: Chapter Five- Statistical Software

Lecture 52 Statistical software Introduction

Lecture 53 Introduction to Jamovi statistical tool

Lecture 54 LAB-03- analysis on synthetical dataset ONE using Jamovi- PART ONE

Lecture 55 LAB-03- analysis on synthetical dataset ONE using Jamovi- PART TWO

Lecture 56 LAB-03- analysis on synthetical dataset TWO using Jamovi- PART TWO

Section 8: Chapter Six- LAB-04- Real Project Analysis with Jamovi

Lecture 57 LAB-04- Overview

Lecture 58 LAB-04- Implementation part one

Lecture 59 LAB-04- Implementation part two

Lecture 60 LAB-04- Implementation part three

Lecture 61 LAB-04- Implementation part four

Lecture 62 LAB-04- Implementation part five

Section 9: Chapter Seve- Closing and Next vision

Lecture 63 Research & Analysis- Review and Vision of Prediction

Lecture 64 References

Section 10: What is Next?

Lecture 65 It's not goodbye but keep in touch!

Lecture 66 Congratulations! and next if you aim for it!

students- post/undergraduate; scholars, scientists, executives, managers, professionals, and layperson

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Udemy - Analysis From the Start to The End